Abstract
Passive surface wave imaging based on noise cross-correlation has been a research hotspot in recent years. However, because randomness of noise is difficult to achieve in reality, prominent noise sources will inevitably affect the dispersion measurement. Additionally, in order to recover high-fidelity surface waves, the time series input during cross-correlation calculation is usually very long, which greatly limits the efficiency of passive surface wave imaging. With an automatic noise or signal removal algorithm based on synchrosqueezed continuous wavelet transform (SS-CWT), these problems can be alleviated. We applied this method to 1-h passive datasets acquired in Sichuan province, China; separated the prominent noise events in the raw field data, and enhanced the cross-correlation reconstructed surface waves, effectively improving the accuracy of the dispersion measurement. Then, using the conventional surface wave inversion method, the shear wave velocity profile of the underground structure in this area was obtained.
Highlights
Passive seismic methods do not require the excitation of explosives, vibroseis, etc.; they require the placement of node geophones according to the active source acquisition line and point spacing, and the data can be collected by continuously receiving ambient noises
Some studies have proven that structures tens to hundreds of meters underground can be imaged with frequencies higher than 1 Hz, such as by using traffic noise to detect urban underground space [17,18], which has attracted the attention of more and more engineering seismologists
Distinct from the above methods, Mousavi et al [25] proposed a new and fast algorithm for accurate noise removal/signal removal based on higher-order statistics (HOS), general cross-validation (GCV), and wavelet hard thresholding (WHT) in synchrosqueezed domains and tested the performance of the proposed algorithm with synthetic and real seismic data
Summary
Passive seismic methods do not require the excitation of explosives, vibroseis, etc.; they require the placement of node geophones according to the active source acquisition line and point spacing, and the data can be collected by continuously receiving ambient noises. Distinct from the above methods, Mousavi et al [25] proposed a new and fast algorithm for accurate noise removal/signal removal based on higher-order statistics (HOS), general cross-validation (GCV), and wavelet hard thresholding (WHT) in synchrosqueezed domains and tested the performance of the proposed algorithm with synthetic and real seismic data He indicated that it can be an effective procedure in ambient noise studies. We applied the noise or signal removal algorithm based on SS-CWT proposed by Mousavi et al [25] to a 1-h passive seismic dataset acquired in Sichuan, China, and verified the effectiveness of this method in enhancing passive surface waves from short time series We used this method to preprocess the 1-hour passive seismic data in Sichuan to remove the prominent noise events and used cross-correlation to produce virtual shot gathers from the preprocessed noise records. The applicability and shortcomings of this method are discussed
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